Skip to main content

Modified diffusion entropy analysis; a temporal complexity analysis method

Project description

Diffusion entropy analysis

pytest status mkdocs status python versions ruff uv

Diffusion Entropy Analysis is a time-series analysis method for detecting temporal scaling in a data set, such as particle motion, a seismograph, or an electroencephalograph signal. Diffusion Entropy Analysis converts a timeseries into a diffusion trajectory and uses the entropy of this trajectory to measure the temporal scaling in the data. This is accomplished by moving a window along the trajectory, then using the relationship between the natural logarithm of the length of the window and the Shannon entropy to extract the scaling of the time-series process.

For further details about the method and how it works, please see Culbreth, G., Baxley, J. and Lambert, D., 2023. Detecting temporal scaling with modified diffusion entropy analysis. arXiv preprint arXiv:2311.11453.

Installation and use

You'll need an up to date Python installation, e.g., through uv. Once you have one, clone this repository to a location on your file system where you can work with the files.

A user guide is available in the documentation.

Built with

numpy scipy polars matplotlib seaborn tqdm pytest ruff material for mkdocs mkdocstrings

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pymdea-0.1.0.tar.gz (101.3 kB view hashes)

Uploaded Source

Built Distribution

pymdea-0.1.0-py3-none-any.whl (8.9 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page